论文标题
在同行评估中检测问题声明
Detecting Problem Statements in Peer Assessments
论文作者
论文摘要
有效的同行评估要求学生注意他们评价的工作中的缺陷。因此，他们的评论应确定问题。但是，有什么方法可以检查他们做什么？我们试图自动化决定评论是否检测到问题的过程。我们使用了18,000多个评论评论，这些评论被评论者标记为检测或未检测到工作问题。我们使用手套和Bert嵌入方式部署了几种传统的机器学习模型以及神经网络模型。我们发现，表现最好的人是分层注意力网络分类器，其次是双向封闭式复发单元（GRU）注意力和胶囊模型，分别分别为93.1％和90.5％。最好的非神经网络模型是支持向量机，得分为89.71％。其次是随机梯度下降模型和89.70％和88.98％的逻辑回归模型。
Effective peer assessment requires students to be attentive to the deficiencies in the work they rate. Thus, their reviews should identify problems. But what ways are there to check that they do? We attempt to automate the process of deciding whether a review comment detects a problem. We use over 18,000 review comments that were labeled by the reviewees as either detecting or not detecting a problem with the work. We deploy several traditional machine-learning models, as well as neural-network models using GloVe and BERT embeddings. We find that the best performer is the Hierarchical Attention Network classifier, followed by the Bidirectional Gated Recurrent Units (GRU) Attention and Capsule model with scores of 93.1% and 90.5% respectively. The best non-neural network model was the support vector machine with a score of 89.71%. This is followed by the Stochastic Gradient Descent model and the Logistic Regression model with 89.70% and 88.98%.
